This review paper evaluates the potential of remote sensing for assessing species diversity, an increasingly urgent task. Existing studies of species distribution patterns using remote sensing can be essentially categorized into three types. The first involves direct mapping of individual plants or associations of single species in relatively large, spatially contiguous units. The second technique involves habitat mapping using remotely sensed data, and predictions of species distribution based on habitat requirements. Finally, establishment of direct relationships between spectral radiance values recorded from remote sensors and species distribution patterns recorded from field observations may assist in assessing species diversity. Direct mapping is applicable over smaller extents, for detailed information on the distribution of certain canopy tree species or associations. Estimations of relationships between spectral values and species distributions may be useful for the limited purpose of indicating areas with higher levels of species diversity, and can be applied over spatial extents of hundreds of square kilometres. Habitat maps appear most capable of providing information on the distributions of large numbers of species in a wider variety of habitat types. This is strongly limited by variation in species composition, and best applied over limited spatial extents of tens of square kilometres.

Using remote sensing to assess biodiversity

The development of a spectral-spatial classifier for earth observational data

RESUMELors de l'analyse par ordinateur d'images aeriennes multispectrales de haute definition ayant pour but l'inventaire forestier, il pourrait etre plus pratique d'utiliser les cimes d'arbre comme objets de notre analyse plutot que les peuplements forestiers ou les simples pixels. En partant de cette proposition, il devient important d'etre capable de definir spectralement ces cimes le plus succinctement possible. Cet article propose, decrit et compare sept manieres differentes de definir spectralement des cimes d'arbre provenant d'images aeriennes de haute resolution spatiale dans le but d'identifier leurs especes forestieres.Parmis les sept types de signatures spectrales testes, cinq menerent a des exactitudes de classification du meme ordre (72 ± 3%) en differentiant cinq especes de coniferes. Une amelioration de l'exactitude de classification (jusqu'a 76 %) fut rendu possible pour certainnes signatures en utilisant une procedure d'analyse canonique avant la classification. Les signatures basees sur ...

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Comparison of Possible Multispectral Classification Schemes for Tree Crowns Individually Delineatedon High Spatial Resolution MEIS Images

An analytical procedure for the design of the spectral channels for multispectral remote sensor systems is defined. An optimal design based on the criterion of minimum mean-square representation error using the Karhunen-Loeve expansion was developed to represent the spectral response functions from a stratum based upon a stochastic process scene model. From the overall pattern recognition system perspective the effect of the representation accuracy on a typical performance criterion, the probability of correct classification, is investigated. The optimum sensor design provides a standard against which practical (suboptimum) operational sensors can be compared. An example design is provided and its performance is illustrated. Although the analytical technique was developed primarily for the purpose of sensor design it was found that the procedure has potential for making important contributions to scene understanding. It was concluded that spectral channels which have narrow bandwidths relative to current sensor systems may be necessary to provide adequate spectral representation and improved classification performance.

/pdf/analytical-design-of-multispectral-sensors-3iz8olrfls.pdf

Analytical Design of Multispectral Sensors

Remote Sensing of the Earth's resources from space-based sensors has evolved in the past 20 years from a scientific experiment to a commonly used technological tool. The scientific applications and engineering aspects of remote sensing systems have been studied extensively. However, most of these studies have been aimed at understanding individual aspects of the remote sensing process while relatively few have studied their interrelations. A motivation for studying these interrelationships has arisen with the advent of highly sophisticated configurable sensors as part of the Earth Observing System (EOS) proposed by NASA for the 1990's. Two approaches to investigating remote sensing systems are developed. In one approach, detailed models of the scene, the sensor, and the processing aspects of the system are implemented in a discrete simulation. This approach is useful in creating simulated images with desired characteristics for use in sensor or processing algorithm development. A less complete, but computationally simpler method based on a parametric model of the system is also developed. In this analytical model the various informational classes are parameterized by their spectral mean vector and covariance matrix. These class statistics are modified by models for the atmosphere, the sensor, and processing algorithms and an estimate made of the resulting classification accuracy among the informational classes. Application of these models is made to the study of the proposed High Resolution Imaging Spectrometer (HRIS). The interrelationships among observational conditions, sensor effects, and processing choices are investigated with several interesting results.

https://ntrs.nasa.gov/api/citations/19900017060/downloads/19900017060.pdf

Modeling, simulation, and analysis of optical remote sensing systems

Multivariate normal probability density functions are usually used in classification decision (i.e., recognition) processes on multispectral scanner data. The mean and covariance matrix of the normal density function are usually estimated from a training data set. In this study, a comparison was made to determine whether or not improved classification results could be obtained by use of a different form to represent the probability density functions, namely, an empirical multivariate probability density histogram of decorrelated variables. First, tests were made of the normality of the individual subsets of data, and all were found to be non-normal at the 1% level of significance using a standard chi-square goodness-of-fit test. Operating characteristic curves then were generated to represent decisions made with each form between each given class and a uniformly distributed alternative class; a uniform distribution was chosen because the results are then least dependent on the choice of the alternative data set. It was found that the probabilities of misclassification using the two forms were approximately identical for almost every data set even though a large number of individual data points were classified differently. It was concluded that a decision rule based on the assumption of multivariate normal distributions of scanner signals performs sufficiently well, in comparison with a more accurate but more complicated rule, to warrant its continued use in recognition processing.

R. B. Crane

Papers

Suitability of the Normal Density Assumption for Processing Multispectral Scanner Data